Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@Article{Can2011,
  author =       "Birkan Can and Cathal Heavey",
  title =        "Comparison of experimental designs for
                 simulation-based symbolic regression of manufacturing
                 systems",
  journal =      "Computer \& Industrial Engineering",
  volume =       "61",
  number =       "3",
  pages =        "447--462",
  month =        oct,
  year =         "2011",
  ISSN =         "0360-8352",
  DOI =          "doi:10.1016/j.cie.2011.03.012",
  URL =          "http://www.sciencedirect.com/science/article/B6V27-52JDFD9-1/2/207e7db7ff221a11f1a808666cba277d",
  keywords =     "genetic algorithms, genetic programming,
                 Meta-modelling, Design of experiments, Discrete-event
                 simulation, Decision support",
  abstract =     "In this article, an empirical analysis of experimental
                 design approaches in simulation-based metamodelling of
                 manufacturing systems with genetic programming (GP) is
                 presented. An advantage of using GP is that prior
                 assumptions on the structure of the metamodels are not
                 required. On the other hand, having an unknown
                 structure necessitates an analysis of the experimental
                 design techniques used to sample the problem domain and
                 capture its characteristics. Therefore, the study
                 presents an empirical analysis of experimental design
                 methods while developing GP metamodels to predict
                 throughput rates in a common industrial system, serial
                 production lines. The objective is to identify a robust
                 sampling approach suitable for GP in simulation-based
                 meta-modelling. Experiments on different sizes of
                 production lines are presented to demonstrate the
                 effects of the experimental designs on the complexity
                 and quality of approximations as well as their
                 variance. The analysis showed that GP delivered
                 system-wide meta-models with good predictive
                 characteristics even with the limited sample data.",
}

Genetic Programming entries for Birkan Can Cathal Heavey

Citations